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Introduction This story begins the day after I got home after giving a talk at the wonderful DevFest Siberia. Shortly after my weekly fix of Westworld, a strange nagging feeling appeared — like the one you get from unpaid bills, a postcard that you forgot to send, or a particularly nasty API endpoint that you were supposed to refactor a year ago but then, well, I mean, it works, right? Then I remembered.

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After developing Glow last year, I came to realize the two limitations of Go for distributed data processing. First, generics are needed. Of course, we can use reflection. But it is noticeably slower, to the point that I do not want to show the performance numbers. Second, dynamic remote code execution is also needed if we want to dynamically adjust the execution plan. We could pre-build all the execution DAGs first and choose one of them during run time.

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Back when crypto/tls was slow and net/http young, the general wisdom was to always put Go servers behind a reverse proxy like NGINX. That’s not necessary anymore! At Cloudflare we recently experimented with exposing pure Go services to the hostile wide area network. With the Go 1.8 release, net/http and crypto/tls proved to be stable, performant and flexible. However, the defaults are tuned for local services. In this articles we’ll see how to tune and harden a Go server for Internet exposure.

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Advanced Encoding and Decoding Techniques Go’s standard library comes packed with some great encoding and decoding packages covering a wide array of encoding schemes. Everything from CSV, XML, JSON, and even gob - a Go specific encoding format - is covered, and all of these packages are incredibly easy to get started with. In fact, most of them don’t require you to add any code at all; You simply plug in your data and it spits out encoded data.

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Go is often the tool of choice for building the guts of a high-performance system, but Go was also designed with some features that are great for building high-level abstractions. You don’t need to switch gears to a dynamic language like Ruby or Python to enjoy pleasant APIs or declarative syntax. It’s an increasingly popular choice to express an API as a DSL - a Domain-Specific Language. A DSL is a language-within-a-language that is compiled or interpreted inside a host language- in our case, Go.

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Dave Cheney covered how interfaces are used to design good Go code in his SOLID Go Design talk and blog post. In this blog post, we are going to focus on some tips on how to write unit tests for that beautiful SOLID code. The primary objectives for our test builds are to: Increase the confidence in our code. Otherwise, the test code is just dead weight. Be fast. Who likes waiting 15 minutes for the tests to pass?

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So far we’ve seen different ways to use Go to build a variety of applications, from versioning your data pipelines to building your own BBQ grill controller. But often times you’ll be looking to reuse the code from others in order to focus on what your code should do and not reinvent the wheel. How do you know which package to use? And when should you use a package versus writing your own?

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This was one of those projects that sat in the back of my mind for quite a while. It was destined to join the many others in my side project graveyard unless I had a good reason to finish it, like a date for a blog post. This post is an explanation and exploration of the goroutree data structure. The structure itself is composed of many separate but coordinating goroutines that make up the tree itself.

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The Saga of Go Dependency Management The Go community is on the cusp of some major shifts in the way we handle dependencies. These shifts are a long time coming, and involve the work and effort of dozens, if not hundreds, of people. Six to twelve months from now, the Go dependency management landscape is likely to look very different. For those who haven’t been following closely - or, honestly, even for those who have - it can be difficult to keep track of what’s going on, and where things are going.

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I know about Gophers, but what is a Pachyderm? Pachyderm is an open source framework, written in Go, for reproducible data processing. With Pachyderm, you can create language agnostic data pipelines where the data input and output of each stage of your pipeline are versioned controlled in Pachyderm’s File System (PFS). Think “git for data.” You can view diffs of your data and collaborate with teammates using Pachyderm commits and branches.